Abstract
Global Climate Models (GCM) play a vital role in assessing the large-scale impacts of climate change. Downscaling methods can translate coarse-resolution climate information from GCM to high-resolution predictions to forecast regional effects. Unfortunately, current downscaling methods struggle to fully take into account spatial relationships among variables, especially at long distances. In this work, we propose an instance-conditional pixel synthesis generative adversarial network (ICPS-GAN), wherein conditioning on spatial information is an explicit way of providing the GAN with previous high-resolution and current low-resolution data, resulting in an enhancement of the general performance. Experimental results on precipitation forecast for US region data outperform both traditional and other learning-based methods when extrapolating in space.
